Abstract
BACKGROUND: 30% of Major Depression Disorder patients display an immune-mediated subtype that is associated with poor response to first-line antidepressant treatments. Immune-targeted augmentation with anti-inflammatory compounds shows promise and may be more effective for the immune-mediated subgroup. For optimal clinical trial designs, we require guidance on the selection of patients who may benefit, on the outcome measures that capture the clinical benefits, and the subtype-specific effect sizes per compound. AIM: Identify baseline predictive blood-based and clinical biomarkers to facilitate predictive enrichment strategies for future clinical trials on immune-mediated depression, define the optimal outcome measures for immune-targeted pharmacological interventions, and ranking these based on their subtype-specific effect sizes. APPROACH: I will address these questions through a dual approach, combining insights gained during the preparation of a new RCT with stratification meta-analyses, design harmonisation and machine learning strategies in existing datasets (n=9 RCTs) within an international consortium. IMPACT: My project will optimise future RCT protocols. This will result in a decreased number of failed RCTs, which leads to cost-effective benefits and more interest among pharmaceutical industry players. The predictive enrichment strategies can then be used to innovate intervention trials also in other mental disorders.
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